• Journal of applied mathematics & informatics
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• v.27 no.3_4
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• pp.621-638
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• 2009

The linear system Ax = b will have (i) no solution, (ii) only one non-trivial (trivial) solution, or (iii) infinity of solutions. Our focus will be on cases (ii) and (iii). The mathematical models of many real-world problems give rise to (a) ill-conditioned linear systems, (b) singular linear systems (A is singular with all its linearly independent rows are sufficiently linearly independent), or (c) ill-conditioned singular linear systems (A is singular with some or all of its strictly linearly independent rows are near-linearly dependent). This article highlights the scope and need of a randomized algorithm for ill-conditioned/singular systems when a reasonably narrow domain of a solution vector is specified. Further, it stresses that with the increasing computing power, the importance of randomized algorithms is also increasing. It also points out that, for many optimization linear/nonlinear problems, randomized algorithms are increasingly dominating the deterministic approaches and, for some problems such as the traveling salesman problem, randomized algorithms are the only alternatives.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.9A
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• pp.856-867
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• 2006

This paper proposes a new algorithm for opportunistic scheduling that take advantage of both multiuser diversity and power control. Motivated by the multicast RTS and priority-based CTS mechanism of OSMA protocol, we propose an opportunistic packet scheduling with power control scheme based on IEEE 802.11 MAC protocol. The scheduling scheme chooses the best candidate receiver for transmission by considering the SINR at the nodes. This mechanism ensures that the transmission would be successful. The power control algorithm on the other hand, helps reduce interference between links and could maximize spatial reuse of the bandwidth. We then formulate a convex optimization problem for minimizing power consumption and maximizing net utility of the system. We showed that if a transmission power vector satisfying the maximum transmission power and SINR constraints of all nodes exist, then there exists an optimal solution that minimizes overall transmission power and maximizes utility of the system.

• Journal of the Korean Society of Mechanical Technology
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• v.20 no.6
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• pp.809-817
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• 2018

The model predictive controller performance of the mobile robot is set to an arbitrary value because it is difficult to select an accurate value with respect to the controller parameter. The general model predictive control uses a quadratic cost function to minimize the difference between the reference tracking error and the predicted trajectory error of the actual robot. In this study, we construct a predictive controller by transforming it into a quadratic programming problem considering velocity and acceleration constraints. The control parameters of the predictive controller, which determines the control performance of the mobile robot, are used a simple weighting matrix Q, R without the reference model matrix $A_r$ by applying a quadratic cost function from which the reference tracking error vector is removed. Therefore, we designed the predictive controller 1 and 2 of the mobile robot considering the constraints, and optimized the controller parameters of the predictive controller using a genetic algorithm with excellent optimization capability.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.285-291
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• 2022

The number of people enrolling in universities is rising due to the simplicity of applying and the benefit of earning a bachelor's degree. However, the on-time graduation rate has declined since plenty of students fail to complete their courses and take longer to get their diplomas. Even though there are various reasons leading to the aforementioned problem, it is crucial to emphasize the cause originating from the management and care of learners. In fact, understanding students' difficult situations and offering timely Number of Test data and advice would help prevent college dropouts or graduate delays. In this study, we present a machine learning-based method for early detection at-risk students, using data obtained from graduates of the Faculty of Information Technology, Dainam University, Vietnam. We experiment with several fundamental machine learning methods before implementing the parameter optimization techniques. In comparison to the other strategies, Random Forest and Grid Search (RF&GS) and Random Forest and Random Search (RF&RS) provided more accurate predictions for identifying at-risk students.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.1
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• pp.12-18
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• 2012

Stereo correspondence is the central problem of stereo vision. In this paper, we propose a stereo matching scheme based on a genetic algorithm using a multi-resolution method and AD-Census. The proposed approach considers the matching environment as an optimization problem and finds the disparity by using a genetic algorithm And adaptive chronosome structure using edge pixels and crossover mechanism are employed in this technique. A cost function is composes of certain constraints whice are commonly used in stereo matching. AD-Census measure is applied to reduce disparity error. To increase the efficiency of process, we apply image pyramid method to stereo matching and calculate the initial disparity map at the coarsest resolution. Then initial disparity map is propagated to the next finer resolution, interpolated and performed disparity refinement using local feature vector. We valid our method not only reduces the search time for correspondence compared with conventional GA-based method but also ensures the validity of matching.

• Information Systems Review
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• v.24 no.4
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• pp.23-40
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• 2022

Although Support Vector Machine(SVM) has been used in various fields such as bankruptcy prediction model, the hyperplane learned by SVM in class imbalance problem can be severely skewed toward minority class and has a negative impact on performance because the area of majority class is expanded while the area of minority class is invaded. This study proposed optimized uneven margin SVM(OPT-UMSVM) combining threshold moving or post scaling method with UMSVM to cope with the limitation of the traditional even margin SVM(EMSVM) in class imbalance problem. OPT-UMSVM readjusted the skewed hyperplane to the majority class and had better generation ability than EMSVM improving the sensitivity of minority class and calculating the optimized performance. To validate OPT-UMSVM, 10-fold cross validations were performed on five sub-datasets with different imbalance ratio values. Empirical results showed two main findings. First, UMSVM had a weak effect on improving the performance of EMSVM in balanced datasets, but it greatly outperformed EMSVM in severely imbalanced datasets. Second, compared to EMSVM and conventional UMSVM, OPT-UMSVM had better performance in both balanced and imbalanced datasets and showed a significant difference performance especially in severely imbalanced datasets.

• Korean Chemical Engineering Research
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• v.59 no.2
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• pp.191-199
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• 2021

A distillation column, which is a main facility of the chemical process, separates the desired product from a mixture by using the difference of boiling points. The distillation process requires the optimization and the prediction of operation because it consumes much energy. The target process of this study is difficult to operate efficiently because the composition of feed flow is not steady according to the supplier. To deal with this problem, we could develop a data-driven model to predict operating conditions. However, data preprocessing is essential to improve the predictive performance of the model because the raw data contains outlier and noise. In this study, after optimizing the predictive model based long-short term memory (LSTM) and Random forest (RF), we used a low-pass filter and one-class support vector machine for data preprocessing and compared predictive performance according to the method and range of the preprocessing. The performance of the predictive model and the effect of the preprocessing is compared by using R2 and RMSE. In the case of LSTM, R2 increased from 0.791 to 0.977 by 23.5%, and RMSE decreased from 0.132 to 0.029 by 78.0%. In the case of RF, R2 increased from 0.767 to 0.938 by 22.3%, and RMSE decreased from 0.140 to 0.050 by 64.3%.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.25-33
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• 2021

With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1825-1834
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• 2022

Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.